This book highlights recent advance in the area of Machine Learning and IoT, and their applications to solve societal issues/problems or useful for various users in the society. It is known that many smart devices are interconnected and the data generated is being analyzed and processed with machine learning models for prediction, classification, etc., to solve human needs in various sectors like health, road safety, agriculture, and education. This contributed book puts together chapters concerning the use of intelligent techniques in various aspects related to the IoT domain from protocols to applications, to give the reader an up-to-date picture of the state-of-the-art on the connection between computational intelligence, machine learning, and IoT.

Autorentext

Dr. Ch. Satyanarayana  is currently working as Professor in the Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada, AP, INDIA. He received his Ph.D. in 2007 from JNTU. He has 20+ years of teaching and research experience and 10 years of administrative experience in various capacities like Controller of Examinations, Head of the Department, and Director Academic and Planning. He has supervised 25 Ph.D. students and 200+ Master's Students. He is Senior Member of IEEE. His research interests include image processing, speech recognition, pattern recognition, network security and big data analytics, and computational intelligence.

Dr. Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China, in 1993 and 1996, respectively. He obtained his D.Sc. (Tech.) degree from the Helsinki University of Technology (now Aalto University), Finland in 1999. In January 2004, he was appointed as Docent (Adjunct Professor) at the same university. He is now working as Professor of data science at the University of Eastern Finland, Finland. He is also Guest Professor at the Harbin Institute of Technology, Beijing Normal University, and Shanghai Maritime University, China. Prof. Gao has published more than 400 technical papers on refereed journals and international conferences, and his current Google Scholar H-index is 32. His research interests are nature-inspired computing methods (e.g., neural networks, fuzzy logic, evolutionary computing, swarm intelligence, and artificial immune systems) with their applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.

Dr. Choo-Yee Ting is currently working as Associate Professor at the Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia. He is also Deputy Dean of the Institute for Postgraduate Studies, Multimedia University. In year 2002, Choo-Yee Ting was awarded the Fellow of Microsoft Research by Microsoft Research Asia, Beijing, China. He has been active in research projects related to predictive analytics and Big Data. Most of the projects were funded by MOE, MOSTI, Telekom Malaysia, MDeC, and industries. In year 2014, he and his team members won two national-level Big Data Analytics competitions. Dr. Ting has been the trainer for MDeC and INTAN for courses related to Big Data and Data Science. He is also Consultant, Mentor, and assessor for projects under MDeC funding. Currently he is working on Trouble Ticket Resolution prediction for Telekom Malaysia, AirAsia seat capacity optimization, and Dengue outbreak prediction for the government of Philippines. Dr. Ting is certified in Microsoft Technology Associate (Database), IBM DB2 CDA, and the Coursera Data Science Specialization (John Hopkins University).

Dr. Naresh Babu Muppalaneni** **is currently working as Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Silchar. He received his M.Tech. from Andhra University and Ph.D. from Acharya Nagarjuna University. He has published more than 30 papers in different International journals, book chapters, conference proceedings, and edited research volumes. He has published 5 volumes in Springer Briefs in Forensic and Medical Bioinformatics. He is Fellow of IETE, Life Member of CSI, Member of ISCA, and Senior Member of IEEE. He is a recipient of Best Teacher Award from JNTU Kakinada. He has completed research projects worth of 2 crore rupees from DST, DRDO. He has organized 6 International Conferences and 4 Workshops. His research interests are artificial intelligence in biomedical engineering, human and machine interaction and applications of intelligent system techniques, social network analysis computational systems biology, bioinformatics, and cryptography.

Inhalt

1.      Machine and Deep learning techniques for Image Classification

2.      IoT based Techniques for  smart Agriculture

3.      Machine and Deep learning techniques for Satellite Image Analysis

4.      Machine and Deep learning techniques for Automatic Speech Recognition

5.      Machine and Deep learning techniques for Object Detection

6.      Machine and Deep learning techniques for Object Segmentation

7.      Machine and Deep learning techniques for Automatic Image Caption Generation

8.      Machine and Deep learning techniques for Automatically Adding Sounds to Silent Movies

9.      Machine and Deep learning techniques for News Aggregation and Fraud News Detection

10.  Machine and Deep learning techniques for Natural Language Processing

11.  Machine and Deep learning techniques for Smart Sign Language Recognition System for Dumb and Deaf People

12.  Machine and Deep learning techniques for Smart Text Reader System for Blind Person

13.  Machine and Deep learning techniques for Automatic Language Translation System

14.  Machine and Deep learning techniques for Automatic Handwriting Generation System

15.  Machine and Deep learning techniques for Biomedical Image Processing Applications

16.  Machine and Deep learning techniques for Healthcare Monitoring System

17.  Machine and Deep learning techniques for Impact of IoT in Biomedical Applications

18.  Emerging Pedagogies of Machine Learning and Deep Learning in Smart Agriculture

19.  Machine and Deep learning techniques in Finance

20.  Machine and Deep Learning Techniques in System Biology 21.  IoT Based Road Safety Applications